Social crowd controllers using reinforcement learning methods
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Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2099.1/22235
Tipus de documentProjecte Final de Màster Oficial
Data2014-07-24
Condicions d'accésAccés obert
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Abstract
Crowd Simulation is an area of research that is present in several disciplines and
industries. Even though the visualization of crowds is an important subject, the
behavior behind it helps to make it believable. Behavioral modeling can be a te-
dious task because the agents have to mimic the complexity of human reactions to
situations. In this work, we propose an alternative model for crowd simulation of
pedestrian movements using Reinforcement Learning methods for low level deci-
sions. Taking the approach of microscopic models, we trained an agent to reach a
goal point while avoiding obstacles that might be on its way, and trying to follow a
coherent path during the walk. Once one agent has learned, its knowledge is passed
to the rest of the members of the crowd by sharing the resulting Q-Table, expecting
the individual behavior and interactions to lead to a crowd behavior. We presented
states sets, an action set and reward functions general enough to adapt to different
environments, allowing us to use the same knowledge in different scenario settings.
MatèriesCollective behavior -- Computer simulation, Crowds -- Computer simulation, Comportament col·lectiu -- Simulació per ordinador, Multituds -- Simulació per ordinador
TitulacióMÀSTER UNIVERSITARI EN COMPUTACIÓ (Pla 2006)
Col·leccions
Fitxers | Descripció | Mida | Format | Visualitza |
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95004.pdf | 5,117Mb | Visualitza/Obre |